DOI QR코드

DOI QR Code

Evaluation of the Amount of Nitrogen Top Dressing Based on Ground-based Remote Sensing for Leaf Perilla (Perilla frutescens) under the Polytunnel House

  • Kang, Seong-Soo (R&D Coordination Division, Rural Development Administration) ;
  • Sung, Jwa-Kyung (Soil & Fertilizer Division, National Institute of Agricultural Sciences, RDA) ;
  • Gong, Hyo-Young (Department of Environmental & Biological Chemistry, Chungbuk National University) ;
  • Jung, Hyung-Jin (Department of Environmental & Biological Chemistry, Chungbuk National University) ;
  • Kim, Yoo-Hak (Soil & Fertilizer Division, National Institute of Agricultural Sciences, RDA) ;
  • Hong, Soon-Dal (Department of Environmental & Biological Chemistry, Chungbuk National University)
  • Received : 2016.09.19
  • Accepted : 2016.10.27
  • Published : 2016.10.31

Abstract

This study was conducted to evaluate the amount of nitrogen (N) top dressing based on the normalized difference vegetation indices (NDVI) by ground based sensors for leaf perilla under the polyethylene house. Experimental design was the randomized complete block design for five N fertilization levels and conventional fertilization with 3 and 4 replications in Gumsan-gun and Milyang-si field, respectively. Dry weight (DW), concentration of N, and amount of N uptake by leaf perilla as well as NDVIs from sensors were measured monthly. Difference of growth characteristics among treatments in Gumsan field was wider than Milyang. SPAD-502 chlorophyll meter reading explained 43.4% of the variability in N content of leaves in Gumsan field at $150^{th}$ day after seedling (DAS) and 45.9% in Milyang at $239^{th}$ DAS. Indexes of red sensor (RNDVI) and amber sensor (ANDVI) at $172^{th}$ day after seedling (DAS) in Gumsan explained 50% and 57% of the variability in N content of leaves. RNDVI and ANDVI at $31^{th}$ DAS in Milyang explained 60% and 65% of the variability in DW of leaves. Based on the relationship between ANDVI and N application rate, ANDVI at $172^{th}$ DAS in Gumsan explained 57% of the variability in N application rate but non significant relationship in Milyang field. Average sufficiency index (SI) calculated from ratio of each measurement index per maximum index of ANDVI at $172^{th}$ DAS in Gumsan explained 73% of the variability in N application rate. Although the relationship between NDVIs and growth characteristics was various upon growing season, SI by NDVIs of ground based remote sensors at top dressing season was thought to be useful index for recommendation of N top dressing rate of leaf perilla.

Keywords

References

  1. Behmann, J., A.K. Mahlein, T. Rumpf, C. Romer, and L. Plumer. 2015. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agric. 16:239-260. https://doi.org/10.1007/s11119-014-9372-7
  2. Choi, J.M. and J.Y. Park. 2007. Growth, deficiency symptom and tissue nutrient contents of Leaf Perilla (Perilla frutesens Britt) as influenced by nitrogen concentrations in the fertigation solution. J. Bio-Env. Con. 16(4):365-371.
  3. Gitelson, A.A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 161(2):165-173. https://doi.org/10.1078/0176-1617-01176
  4. Hussain, F., K.F. Bronson, Yadvinder-Singh, Bijay-Singh, and S. Peng. 2000. Use chlorophyll meter sufficiency indices for nitrogen management of irrigated rice in Asia. Agron. J. 92:875-879.
  5. Jung, K.S., K.H. Jung, W.K. Park, Y.S. Song, and K.H. Kim. 2010. Establishment of the optimum nitrogen application rates for oriental melon at various growth stages with a fertigation system in a plastic film house. Korean J. Soil Sci. Fert. 43(3):349-355.
  6. Kang, S.S. 2007. Evaluation for biomass and nitrogen nutrition of crops by reflectance indices of ground-based remote sensors. Ph. D. Thesis, Chungbuk National University. Cheongju, Korea.
  7. Kang, S.S., J.Y. Lee, J.K. Sung, H.Y. Gong, H.J. Jung, C.H. Park, Y.U. Yun, M.S. Kim, and Y.H. Kim. 2011. Recommendation of the amount of nitrogen top dressing based on soil nitrate nitrogen content for leaf perilla (Perilla frutescens) under the Plastic Film House. Korean J. Soil Sci. Fert. 44(6):1112-1117. https://doi.org/10.7745/KJSSF.2011.44.6.1112
  8. Kang, S.S., A.S. Roh, S.C. Choi, Y.S. Kim, H.J. Kim, M.T. Choi, B.G. Ahn, H.K. Kim, S.J. Park, Y.H. Lee, S.H. Yang, J.S. Ryu, Y.G. Sohn, M.S. Kim, M.S. Kong, C.H. Lee, D.B. Lee, and Y. H. Kim. 2013. Status and Change in Chemical Properties of Polytunnel Soil in Korea from 2000 to 2012. Korean J. Soil Sci. Fert. 46(6):641-646. https://doi.org/10.7745/KJSSF.2013.46.6.641
  9. Kim, H.K., J.S. Oh, D.S. Chung, W.B. Chung, S.J. Jeong, Y.B. Yi, and D.H. Kim. 2003. Difference of yield components according to application levels, seeding methods and seeding date in Leaf Perilla. J. Life Sci. 13(6):782-787. https://doi.org/10.5352/JLS.2003.13.6.782
  10. Kim, J.J., S.S. Kang, K.I. Kim, and S.D. Hong. 2006. Relationship among chemical properties of soils with different texture taken from plastic film house of chungbuk area. Korean J. Soil Sci. Fert. 39(3):144-150.
  11. Kim, K.D., J.W. Lee, I.H. Cho, T.Y. Kim, Y.H. Woo, E.Y. Nam, and B.H. Mun. 2004. Determination of daily amount of N and K required in various growth stage and establishment of diagnostic criteria using petiole sap analysis in the semi-forcing culture of cucumber. J. Bio. Environ. Con. 13:96-101.
  12. Lee, J.Y., J.K. Sung, S.S. Kang, B.C. Jang., S.Y. Lee, R.Y. Kim and Y.J. Lee. 2012. Contents of inorganic nutrient in leaf perilla in growing stages under plastic film house cultivation. Korean J. Soil Sci. Fert. 45(2):215-222. https://doi.org/10.7745/KJSSF.2012.45.2.215
  13. Lee, W.S., V. Alchanatis, C. Yang, M. Hirafuji, D. Moshou, and C. Li. 2010. Sensing technologies for precision specialty crop production. Comput. Electron. Agric. 74:2-33. https://doi.org/10.1016/j.compag.2010.08.005
  14. Lin, C., S.C. Popescu, S.C. Huang, P.T. Chang, and H.L. Wen. 2015. A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves. Biogeosci. 12:49-66. https://doi.org/10.5194/bg-12-49-2015
  15. NAAS. 2010. Fertilizer application recommendations for crop plants, National Academy of Agricultural Science, RDA, Suwon, Korea.
  16. NIAST. 2000. Methods of soil and plant analysis. National Institute of Agricultural Science and Technology, RDA, Suwon, Korea.
  17. NIAST. 2010. Methods of soil chemical analysis. National Institute of Agricultural Science and Technology, RDA, Suwon, Korea.
  18. Parry, C., J.M.Jr. Blonquist, and B. Bugbee. 2014. In situ measurement of leaf chlorophyll concentration: analysis of the optical/absolute relationship. Plant Cell Environ. 37:2508-2520. https://doi.org/10.1111/pce.12324
  19. Peterson, T.A., T.M. Blackmer, D.D. Francis, and J.S. Schepers. 1993. Using a chlorophyll meter to improve N management. Nebguide G93-1171A. Coop. Ext. Serv., Univ. of Nebraska, Lincoln.
  20. RDA. 2010. Nongsaro. On-site soil nitrate testing using nitrate test strip. http://www.nongsaro.go.kr (retrieved on September 18, 2016) (In Korean).
  21. RDA. 2015. Nongsaro. Selection of perilla variety. http://www.nongsaro.go.kr (In Korean).
  22. Uddling, J., J. Gelang-Alfredsson, K. Piikki, and H. Pleijel. 2007. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth. Res. 91:37-46. https://doi.org/10.1007/s11120-006-9077-5
  23. Varvel, G.E., J.S. Schepers, and D.D. Francis. 1997. Ability for in-season correction of nitrogen deficiency in corn using chlorophyll meters. Soil Sci. Soc. Am. J. 61:1233-1239. https://doi.org/10.2136/sssaj1997.03615995006100040032x